





"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xlnet import XLNetTokenizer
else:
       SCREAMING_SNAKE_CASE__   =  None


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__   =  {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

SCREAMING_SNAKE_CASE__   =  "▁"

# Segments (not really needed)
SCREAMING_SNAKE_CASE__   =  0
SCREAMING_SNAKE_CASE__   =  1
SCREAMING_SNAKE_CASE__   =  2
SCREAMING_SNAKE_CASE__   =  3
SCREAMING_SNAKE_CASE__   =  4

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = 'left'
      _SCREAMING_SNAKE_CASE							      = XLNetTokenizer





      def __init__(			self ,		lowercase=None ,		lowercase=None ,		lowercase=False ,		lowercase=True ,		lowercase=False ,		lowercase="<s>" ,		lowercase="</s>" ,		lowercase="<unk>" ,		lowercase="<sep>" ,		lowercase="<pad>" ,		lowercase="<cls>" ,		lowercase="<mask>" ,		lowercase=["<eop>", "<eod>"] ,		**lowercase ,		)						->	str:
            # Mask token behave like a normal word, i.e. include the space before it
            lowerCAmelCase    		=			AddedToken(lowercase ,		lstrip=lowercase ,		rstrip=lowercase					) if isinstance(lowercase ,		lowercase					) else mask_token

            super().__init__(
                vocab_file=lowercase ,		tokenizer_file=lowercase ,		do_lower_case=lowercase ,		remove_space=lowercase ,		keep_accents=lowercase ,		bos_token=lowercase ,		eos_token=lowercase ,		unk_token=lowercase ,		sep_token=lowercase ,		pad_token=lowercase ,		cls_token=lowercase ,		mask_token=lowercase ,		additional_special_tokens=lowercase ,		**lowercase ,		)

            lowerCAmelCase    		=			3
            lowerCAmelCase    		=			do_lower_case
            lowerCAmelCase    		=			remove_space
            lowerCAmelCase    		=			keep_accents
            lowerCAmelCase    		=			vocab_file
            lowerCAmelCase    		=			False if not self.vocab_file else True





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.sep_token_id]
            lowerCAmelCase    		=			[self.cls_token_id]
            if token_ids_a is None:
                  return token_ids_a + sep + cls
            return token_ids_a + sep + token_ids_a + sep + cls





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.sep_token_id]
            lowerCAmelCase    		=			[2]

            if token_ids_a is None:
                  return len(token_ids_a + sep					) * [0] + cls_segment_id
            return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                      """tokenizer."""					)

            if not os.path.isdir(lowercase					):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
                  return
            lowerCAmelCase    		=			os.path.join(
                lowercase ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

            if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase					):
                  copyfile(self.vocab_file ,		lowercase					)

            return (out_vocab_file,)













"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






